Overview

Dataset statistics

Number of variables27
Number of observations1000
Missing cells5364
Missing cells (%)19.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory211.1 KiB
Average record size in memory216.1 B

Variable types

CAT15
NUM11
UNSUPPORTED1

Warnings

Date Reported has a high cardinality: 864 distinct values High cardinality
Date Occurred has a high cardinality: 858 distinct values High cardinality
Crime Code Description has a high cardinality: 70 distinct values High cardinality
MO Codes has a high cardinality: 593 distinct values High cardinality
Premise Description has a high cardinality: 77 distinct values High cardinality
Address has a high cardinality: 935 distinct values High cardinality
Cross Street has a high cardinality: 154 distinct values High cardinality
Location has a high cardinality: 954 distinct values High cardinality
DR Number is highly correlated with df_index and 1 other fieldsHigh correlation
df_index is highly correlated with DR Number and 1 other fieldsHigh correlation
Reporting District is highly correlated with Area ID and 1 other fieldsHigh correlation
Area ID is highly correlated with Reporting District and 1 other fieldsHigh correlation
Crime Code 1 is highly correlated with Crime Code and 1 other fieldsHigh correlation
Crime Code is highly correlated with Crime Code 1 and 1 other fieldsHigh correlation
Crime Code 3 is highly correlated with df_index and 9 other fieldsHigh correlation
Time Occurred is highly correlated with Crime Code 3High correlation
Victim Age is highly correlated with Crime Code 3High correlation
Premise Code is highly correlated with Crime Code 3High correlation
Crime Code 2 is highly correlated with Crime Code 3High correlation
Status Description is highly correlated with Status CodeHigh correlation
Status Code is highly correlated with Status DescriptionHigh correlation
MO Codes has 110 (11.0%) missing values Missing
Victim Sex has 94 (9.4%) missing values Missing
Victim Descent has 94 (9.4%) missing values Missing
Weapon Used Code has 659 (65.9%) missing values Missing
Weapon Description has 659 (65.9%) missing values Missing
Crime Code 2 has 931 (93.1%) missing values Missing
Crime Code 3 has 998 (99.8%) missing values Missing
Crime Code 4 has 1000 (100.0%) missing values Missing
Cross Street has 817 (81.7%) missing values Missing
Date Reported is uniformly distributed Uniform
Date Occurred is uniformly distributed Uniform
Crime Code 3 is uniformly distributed Uniform
Address is uniformly distributed Uniform
Cross Street is uniformly distributed Uniform
Location is uniformly distributed Uniform
df_index has unique values Unique
DR Number has unique values Unique
Crime Code 4 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Victim Age has 169 (16.9%) zeros Zeros

Reproduction

Analysis started2021-08-09 13:56:57.839862
Analysis finished2021-08-09 13:57:45.130500
Duration47.29 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean987928.7
Minimum548
Maximum1993248
Zeros0
Zeros (%)0.0%
Memory size7.8 KiB
2021-08-09T14:57:45.246991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum548
5-th percentile115864
Q1478503.5
median997321
Q31475642.5
95-th percentile1897505.3
Maximum1993248
Range1992700
Interquartile range (IQR)997139

Descriptive statistics

Standard deviation572949.2855
Coefficient of variation (CV)0.5799500364
Kurtosis-1.200724843
Mean987928.7
Median Absolute Deviation (MAD)498564.5
Skewness0.03807213977
Sum987928700
Variance3.282708838e+11
MonotocityNot monotonic
2021-08-09T14:57:45.395371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
187187110.1%
 
147728210.1%
 
144863610.1%
 
183570410.1%
 
185265010.1%
 
104517310.1%
 
137899610.1%
 
180293110.1%
 
87447110.1%
 
139537110.1%
 
Other values (990)99099.0%
 
ValueCountFrequency (%) 
54810.1%
 
141010.1%
 
167010.1%
 
278010.1%
 
931610.1%
 
ValueCountFrequency (%) 
199324810.1%
 
199241710.1%
 
199134910.1%
 
198314010.1%
 
197438910.1%
 

DR Number
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144863519.5
Minimum100118167
Maximum192111869
Zeros0
Zeros (%)0.0%
Memory size7.8 KiB
2021-08-09T14:57:45.549381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum100118167
5-th percentile101096442
Q1121015744.2
median150209692
Q3170514365
95-th percentile190209416.4
Maximum192111869
Range91993702
Interquartile range (IQR)49498620.75

Descriptive statistics

Standard deviation27553414.89
Coefficient of variation (CV)0.1902025781
Kurtosis-1.196783376
Mean144863519.5
Median Absolute Deviation (MAD)21299527.5
Skewness-0.04636664928
Sum1.448635195e+11
Variance7.591906719e+14
MonotocityNot monotonic
2021-08-09T14:57:45.684920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
17211186710.1%
 
13201886710.1%
 
12040059310.1%
 
14050990110.1%
 
19071047610.1%
 
19150714710.1%
 
12020064710.1%
 
18141255210.1%
 
11022016910.1%
 
11170527810.1%
 
Other values (990)99099.0%
 
ValueCountFrequency (%) 
10011816710.1%
 
10020679110.1%
 
10020888510.1%
 
10021722710.1%
 
10021815410.1%
 
ValueCountFrequency (%) 
19211186910.1%
 
19211041610.1%
 
19210852210.1%
 
19191033710.1%
 
19180463310.1%
 

Date Reported
Categorical

HIGH CARDINALITY
UNIFORM

Distinct864
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
2010-08-09T00:00:00
 
4
2015-11-15T00:00:00
 
3
2013-01-12T00:00:00
 
3
2010-11-09T00:00:00
 
3
2013-03-20T00:00:00
 
3
Other values (859)
984 
ValueCountFrequency (%) 
2010-08-09T00:00:0040.4%
 
2015-11-15T00:00:0030.3%
 
2013-01-12T00:00:0030.3%
 
2010-11-09T00:00:0030.3%
 
2013-03-20T00:00:0030.3%
 
2018-02-05T00:00:0030.3%
 
2010-10-15T00:00:0030.3%
 
2011-06-04T00:00:0030.3%
 
2012-07-03T00:00:0030.3%
 
2016-07-14T00:00:0030.3%
 
Other values (854)96996.9%
 
2021-08-09T14:57:45.851499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique740 ?
Unique (%)74.0%
2021-08-09T14:57:46.005420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length19
Mean length19.156
Min length19

Date Occurred
Categorical

HIGH CARDINALITY
UNIFORM

Distinct858
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
2011-01-01T00:00:00
 
4
2010-12-03T00:00:00
 
4
2011-01-26T00:00:00
 
3
2011-11-08T00:00:00
 
3
2010-12-01T00:00:00
 
3
Other values (853)
983 
ValueCountFrequency (%) 
2011-01-01T00:00:0040.4%
 
2010-12-03T00:00:0040.4%
 
2011-01-26T00:00:0030.3%
 
2011-11-08T00:00:0030.3%
 
2010-12-01T00:00:0030.3%
 
2013-01-12T00:00:0030.3%
 
2017-03-15T00:00:0030.3%
 
2010-11-14T00:00:0030.3%
 
2015-06-14T00:00:0030.3%
 
2015-11-20T00:00:0030.3%
 
Other values (848)96896.8%
 
2021-08-09T14:57:46.170665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique728 ?
Unique (%)72.8%
2021-08-09T14:57:46.314643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length19
Mean length19
Min length19

Time Occurred
Real number (ℝ≥0)

HIGH CORRELATION

Distinct227
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1346.928
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Memory size7.8 KiB
2021-08-09T14:57:46.433993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile100
Q1920
median1400
Q31900
95-th percentile2300
Maximum2359
Range2358
Interquartile range (IQR)980

Descriptive statistics

Standard deviation666.2990299
Coefficient of variation (CV)0.4946805099
Kurtosis-0.7790123195
Mean1346.928
Median Absolute Deviation (MAD)500
Skewness-0.4208786818
Sum1346928
Variance443954.3972
MonotocityNot monotonic
2021-08-09T14:57:46.584733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1200525.2%
 
2200343.4%
 
1900323.2%
 
1000262.6%
 
1800252.5%
 
1500242.4%
 
2300232.3%
 
1222.2%
 
1700222.2%
 
2100212.1%
 
Other values (217)71971.9%
 
ValueCountFrequency (%) 
1222.2%
 
560.6%
 
1040.4%
 
1520.2%
 
2010.1%
 
ValueCountFrequency (%) 
235910.1%
 
235520.2%
 
235020.2%
 
234540.4%
 
234030.3%
 

Area ID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct21
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.003
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Memory size7.8 KiB
2021-08-09T14:57:46.718042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median11.5
Q316
95-th percentile20
Maximum21
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.024224621
Coefficient of variation (CV)0.5475074635
Kurtosis-1.218719532
Mean11.003
Median Absolute Deviation (MAD)5.5
Skewness-0.02336680459
Sum11003
Variance36.29128228
MonotocityNot monotonic
2021-08-09T14:57:46.838724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%) 
3696.9%
 
12686.8%
 
2595.9%
 
13575.7%
 
17575.7%
 
20525.2%
 
15515.1%
 
14484.8%
 
19474.7%
 
8464.6%
 
Other values (11)44644.6%
 
ValueCountFrequency (%) 
1323.2%
 
2595.9%
 
3696.9%
 
4404.0%
 
5464.6%
 
ValueCountFrequency (%) 
21404.0%
 
20525.2%
 
19474.7%
 
18444.4%
 
17575.7%
 

Area Name
Categorical

Distinct21
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
Southwest
 
69
77th Street
 
68
Rampart
 
59
Newton
 
57
Devonshire
 
57
Other values (16)
690 
ValueCountFrequency (%) 
Southwest696.9%
 
77th Street686.8%
 
Rampart595.9%
 
Newton575.7%
 
Devonshire575.7%
 
Olympic525.2%
 
N Hollywood515.1%
 
Pacific484.8%
 
Mission474.7%
 
West LA464.6%
 
Other values (11)44644.6%
 
2021-08-09T14:57:46.998784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-08-09T14:57:47.126840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length8
Mean length8.336
Min length6

Reporting District
Real number (ℝ≥0)

HIGH CORRELATION

Distinct601
Distinct (%)60.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1147.689
Minimum119
Maximum2187
Zeros0
Zeros (%)0.0%
Memory size7.8 KiB
2021-08-09T14:57:47.283463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum119
5-th percentile235
Q1628
median1196
Q31681.75
95-th percentile2071
Maximum2187
Range2068
Interquartile range (IQR)1053.75

Descriptive statistics

Standard deviation602.794183
Coefficient of variation (CV)0.5252243274
Kurtosis-1.221347045
Mean1147.689
Median Absolute Deviation (MAD)539
Skewness-0.01626561609
Sum1147689
Variance363360.8271
MonotocityNot monotonic
2021-08-09T14:57:47.403710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
36380.8%
 
168480.8%
 
25660.6%
 
176460.6%
 
63660.6%
 
126760.6%
 
23550.5%
 
35850.5%
 
202950.5%
 
215650.5%
 
Other values (591)94094.0%
 
ValueCountFrequency (%) 
11920.2%
 
12410.1%
 
12710.1%
 
13210.1%
 
13510.1%
 
ValueCountFrequency (%) 
218710.1%
 
218320.2%
 
217730.3%
 
217510.1%
 
215810.1%
 

Crime Code
Real number (ℝ≥0)

HIGH CORRELATION

Distinct70
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean508.288
Minimum121
Maximum956
Zeros0
Zeros (%)0.0%
Memory size7.8 KiB
2021-08-09T14:57:47.534762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum121
5-th percentile230
Q1330
median442
Q3626
95-th percentile928
Maximum956
Range835
Interquartile range (IQR)296

Descriptive statistics

Standard deviation207.0217696
Coefficient of variation (CV)0.4072922626
Kurtosis-0.7412935396
Mean508.288
Median Absolute Deviation (MAD)132
Skewness0.4900477984
Sum508288
Variance42858.01307
MonotocityNot monotonic
2021-08-09T14:57:47.664765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
624909.0%
 
440878.7%
 
330828.2%
 
510818.1%
 
310767.6%
 
626626.2%
 
354515.1%
 
740474.7%
 
745414.1%
 
230393.9%
 
Other values (60)34434.4%
 
ValueCountFrequency (%) 
12140.4%
 
12210.1%
 
210383.8%
 
22030.3%
 
230393.9%
 
ValueCountFrequency (%) 
95670.7%
 
94910.1%
 
946101.0%
 
94010.1%
 
93310.1%
 

Crime Code Description
Categorical

HIGH CARDINALITY

Distinct70
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
BATTERY - SIMPLE ASSAULT
90 
THEFT PLAIN - PETTY ($950 & UNDER)
87 
BURGLARY FROM VEHICLE
82 
VEHICLE - STOLEN
81 
BURGLARY
76 
Other values (65)
584 
ValueCountFrequency (%) 
BATTERY - SIMPLE ASSAULT909.0%
 
THEFT PLAIN - PETTY ($950 & UNDER)878.7%
 
BURGLARY FROM VEHICLE828.2%
 
VEHICLE - STOLEN818.1%
 
BURGLARY767.6%
 
INTIMATE PARTNER - SIMPLE ASSAULT626.2%
 
THEFT OF IDENTITY515.1%
 
VANDALISM - FELONY ($400 & OVER, ALL CHURCH VANDALISMS)474.7%
 
VANDALISM - MISDEAMEANOR ($399 OR UNDER)414.1%
 
ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT393.9%
 
Other values (60)34434.4%
 
2021-08-09T14:57:48.023087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique22 ?
Unique (%)2.2%
2021-08-09T14:57:48.166101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length56
Median length24
Mean length28.541
Min length5

MO Codes
Categorical

HIGH CARDINALITY
MISSING

Distinct593
Distinct (%)66.6%
Missing110
Missing (%)11.0%
Memory size7.8 KiB
0344
110 
0329
 
26
0416
 
20
1501
 
16
0329 1300
 
12
Other values (588)
706 
ValueCountFrequency (%) 
034411011.0%
 
0329262.6%
 
0416202.0%
 
1501161.6%
 
0329 1300121.2%
 
0325111.1%
 
0344 160990.9%
 
0329 130780.8%
 
037770.7%
 
0416 200060.6%
 
Other values (583)66566.5%
 
(Missing)11011.0%
 
2021-08-09T14:57:48.314197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique532 ?
Unique (%)59.8%
2021-08-09T14:57:48.469106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length49
Median length9
Mean length12.02
Min length3

Victim Age
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct79
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.758
Minimum-1
Maximum86
Zeros169
Zeros (%)16.9%
Memory size7.8 KiB
2021-08-09T14:57:48.610893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q120
median31
Q346.25
95-th percentile65
Maximum86
Range87
Interquartile range (IQR)26.25

Descriptive statistics

Standard deviation20.26435491
Coefficient of variation (CV)0.6380866211
Kurtosis-0.631282624
Mean31.758
Median Absolute Deviation (MAD)13
Skewness0.03777424644
Sum31758
Variance410.6440801
MonotocityNot monotonic
2021-08-09T14:57:48.751508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
016916.9%
 
23272.7%
 
30272.7%
 
32252.5%
 
31242.4%
 
21232.3%
 
25232.3%
 
27222.2%
 
28222.2%
 
33212.1%
 
Other values (69)61761.7%
 
ValueCountFrequency (%) 
-110.1%
 
016916.9%
 
310.1%
 
630.3%
 
830.3%
 
ValueCountFrequency (%) 
8610.1%
 
8520.2%
 
8310.1%
 
8110.1%
 
8010.1%
 

Victim Sex
Categorical

MISSING

Distinct3
Distinct (%)0.3%
Missing94
Missing (%)9.4%
Memory size7.8 KiB
M
454 
F
424 
X
 
28
ValueCountFrequency (%) 
M45445.4%
 
F42442.4%
 
X282.8%
 
(Missing)949.4%
 
2021-08-09T14:57:48.894554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-08-09T14:57:48.981166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:49.088129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.188
Min length1

Victim Descent
Categorical

MISSING

Distinct12
Distinct (%)1.3%
Missing94
Missing (%)9.4%
Memory size7.8 KiB
H
342 
W
231 
B
168 
O
98 
X
40 
Other values (7)
 
27
ValueCountFrequency (%) 
H34234.2%
 
W23123.1%
 
B16816.8%
 
O989.8%
 
X404.0%
 
A181.8%
 
K40.4%
 
I10.1%
 
U10.1%
 
C10.1%
 
Other values (2)20.2%
 
(Missing)949.4%
 
2021-08-09T14:57:49.238108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique5 ?
Unique (%)0.6%
2021-08-09T14:57:49.380150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.188
Min length1

Premise Code
Real number (ℝ≥0)

HIGH CORRELATION

Distinct77
Distinct (%)7.7%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean324.019019
Minimum101
Maximum964
Zeros0
Zeros (%)0.0%
Memory size7.8 KiB
2021-08-09T14:57:49.523885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1102
median222
Q3501
95-th percentile708
Maximum964
Range863
Interquartile range (IQR)399

Descriptive statistics

Standard deviation217.5129477
Coefficient of variation (CV)0.6712968527
Kurtosis-1.257899182
Mean324.019019
Median Absolute Deviation (MAD)121
Skewness0.3356349188
Sum323695
Variance47311.8824
MonotocityNot monotonic
2021-08-09T14:57:49.655778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
10122322.3%
 
50120220.2%
 
50213413.4%
 
108626.2%
 
102525.2%
 
203484.8%
 
122414.1%
 
707222.2%
 
210212.1%
 
404151.5%
 
Other values (67)17917.9%
 
ValueCountFrequency (%) 
10122322.3%
 
102525.2%
 
10330.3%
 
104131.3%
 
108626.2%
 
ValueCountFrequency (%) 
96410.1%
 
95110.1%
 
91210.1%
 
90410.1%
 
83410.1%
 

Premise Description
Categorical

HIGH CARDINALITY

Distinct77
Distinct (%)7.7%
Missing1
Missing (%)0.1%
Memory size7.8 KiB
STREET
223 
SINGLE FAMILY DWELLING
202 
MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)
134 
PARKING LOT
62 
SIDEWALK
52 
Other values (72)
326 
ValueCountFrequency (%) 
STREET22322.3%
 
SINGLE FAMILY DWELLING20220.2%
 
MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)13413.4%
 
PARKING LOT626.2%
 
SIDEWALK525.2%
 
OTHER BUSINESS484.8%
 
VEHICLE, PASSENGER/TRUCK414.1%
 
GARAGE/CARPORT222.2%
 
RESTAURANT/FAST FOOD212.1%
 
DEPARTMENT STORE151.5%
 
Other values (67)17917.9%
 
2021-08-09T14:57:49.819105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique34 ?
Unique (%)3.4%
2021-08-09T14:57:49.969856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length57
Median length14
Mean length18.303
Min length3

Weapon Used Code
Real number (ℝ≥0)

MISSING

Distinct27
Distinct (%)7.9%
Missing659
Missing (%)65.9%
Infinite0
Infinite (%)0.0%
Mean375.3049853
Minimum101
Maximum515
Zeros0
Zeros (%)0.0%
Memory size7.8 KiB
2021-08-09T14:57:50.102906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile104
Q1400
median400
Q3400
95-th percentile511
Maximum515
Range414
Interquartile range (IQR)0

Descriptive statistics

Standard deviation112.1436091
Coefficient of variation (CV)0.2988066066
Kurtosis0.8148100971
Mean375.3049853
Median Absolute Deviation (MAD)0
Skewness-1.212843017
Sum127979
Variance12576.18906
MonotocityNot monotonic
2021-08-09T14:57:50.225457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%) 
40020120.1%
 
500363.6%
 
511272.7%
 
102151.5%
 
200131.3%
 
10970.7%
 
20760.6%
 
30840.4%
 
30740.4%
 
30240.4%
 
Other values (17)242.4%
 
(Missing)65965.9%
 
ValueCountFrequency (%) 
10120.2%
 
102151.5%
 
10420.2%
 
10630.3%
 
10970.7%
 
ValueCountFrequency (%) 
51510.1%
 
51310.1%
 
51210.1%
 
511272.7%
 
50410.1%
 

Weapon Description
Categorical

MISSING

Distinct27
Distinct (%)7.9%
Missing659
Missing (%)65.9%
Memory size7.8 KiB
STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)
201 
UNKNOWN WEAPON/OTHER WEAPON
36 
VERBAL THREAT
27 
HAND GUN
 
15
KNIFE WITH BLADE 6INCHES OR LESS
 
13
Other values (22)
49 
ValueCountFrequency (%) 
STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)20120.1%
 
UNKNOWN WEAPON/OTHER WEAPON363.6%
 
VERBAL THREAT272.7%
 
HAND GUN151.5%
 
KNIFE WITH BLADE 6INCHES OR LESS131.3%
 
SEMI-AUTOMATIC PISTOL70.7%
 
OTHER KNIFE60.6%
 
STICK40.4%
 
BLUNT INSTRUMENT40.4%
 
VEHICLE40.4%
 
Other values (17)242.4%
 
(Missing)65965.9%
 
2021-08-09T14:57:50.393915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique12 ?
Unique (%)3.5%
2021-08-09T14:57:50.544300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length46
Median length3
Mean length13.735
Min length3

Status Code
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
IC
779 
AA
109 
AO
105 
JA
 
6
JO
 
1
ValueCountFrequency (%) 
IC77977.9%
 
AA10910.9%
 
AO10510.5%
 
JA60.6%
 
JO10.1%
 
2021-08-09T14:57:50.674259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.1%
2021-08-09T14:57:50.767498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:50.883850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Status Description
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
Invest Cont
779 
Adult Arrest
109 
Adult Other
105 
Juv Arrest
 
6
Juv Other
 
1
ValueCountFrequency (%) 
Invest Cont77977.9%
 
Adult Arrest10910.9%
 
Adult Other10510.5%
 
Juv Arrest60.6%
 
Juv Other10.1%
 
2021-08-09T14:57:51.029809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.1%
2021-08-09T14:57:51.137503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:51.279344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length12
Median length11
Mean length11.101
Min length9

Crime Code 1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct69
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean508.279
Minimum121
Maximum956
Zeros0
Zeros (%)0.0%
Memory size7.8 KiB
2021-08-09T14:57:51.420223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum121
5-th percentile230
Q1330
median442
Q3626
95-th percentile928
Maximum956
Range835
Interquartile range (IQR)296

Descriptive statistics

Standard deviation207.0083564
Coefficient of variation (CV)0.4072730851
Kurtosis-0.7411983295
Mean508.279
Median Absolute Deviation (MAD)132
Skewness0.4899837297
Sum508279
Variance42852.45962
MonotocityNot monotonic
2021-08-09T14:57:51.557411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
624909.0%
 
440878.7%
 
330828.2%
 
510818.1%
 
310767.6%
 
626626.2%
 
354515.1%
 
740474.7%
 
745414.1%
 
230393.9%
 
Other values (59)34434.4%
 
ValueCountFrequency (%) 
12140.4%
 
12210.1%
 
210383.8%
 
22030.3%
 
230393.9%
 
ValueCountFrequency (%) 
95670.7%
 
94910.1%
 
946101.0%
 
94010.1%
 
93310.1%
 

Crime Code 2
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct13
Distinct (%)18.8%
Missing931
Missing (%)93.1%
Infinite0
Infinite (%)0.0%
Mean944.3913043
Minimum480
Maximum998
Zeros0
Zeros (%)0.0%
Memory size7.8 KiB
2021-08-09T14:57:51.677411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum480
5-th percentile624.8
Q1998
median998
Q3998
95-th percentile998
Maximum998
Range518
Interquartile range (IQR)0

Descriptive statistics

Standard deviation128.6806601
Coefficient of variation (CV)0.1362577773
Kurtosis4.542303734
Mean944.3913043
Median Absolute Deviation (MAD)0
Skewness-2.383914463
Sum65163
Variance16558.71228
MonotocityNot monotonic
2021-08-09T14:57:51.776500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
998565.6%
 
62420.2%
 
94610.1%
 
65310.1%
 
86010.1%
 
93010.1%
 
51010.1%
 
82110.1%
 
62610.1%
 
64910.1%
 
Other values (3)30.3%
 
(Missing)93193.1%
 
ValueCountFrequency (%) 
48010.1%
 
51010.1%
 
62420.2%
 
62610.1%
 
64910.1%
 
ValueCountFrequency (%) 
998565.6%
 
94610.1%
 
93010.1%
 
86010.1%
 
82110.1%
 

Crime Code 3
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing998
Missing (%)99.8%
Memory size7.8 KiB
999
998
ValueCountFrequency (%) 
99910.1%
 
99810.1%
 
(Missing)99899.8%
 
2021-08-09T14:57:51.895295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)100.0%
2021-08-09T14:57:51.974303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:52.061349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length3
Mean length3.004
Min length3

Crime Code 4
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing1000
Missing (%)100.0%
Memory size7.9 KiB

Address
Categorical

HIGH CARDINALITY
UNIFORM

Distinct935
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
SHERMAN WY
 
5
6TH ST
 
3
1ST ST
 
3
6700 11TH AV
 
3
3RD
 
3
Other values (930)
983 
ValueCountFrequency (%) 
SHERMAN WY50.5%
 
6TH ST30.3%
 
1ST ST30.3%
 
6700 11TH AV30.3%
 
3RD30.3%
 
9300 TAMPA AV30.3%
 
WASHINGTON30.3%
 
NORMANDIE AV20.2%
 
13100 BROMONT AV20.2%
 
1600 WILSHIRE BL20.2%
 
Other values (925)97197.1%
 
2021-08-09T14:57:52.223813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique879 ?
Unique (%)87.9%
2021-08-09T14:57:52.392482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length40
Median length39
Mean length35.46
Min length3

Cross Street
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct154
Distinct (%)84.2%
Missing817
Missing (%)81.7%
Memory size7.8 KiB
CENTRAL AV
 
5
BROADWAY
 
5
VERMONT AV
 
4
MAIN
 
4
AVALON
 
3
Other values (149)
162 
ValueCountFrequency (%) 
CENTRAL AV50.5%
 
BROADWAY50.5%
 
VERMONT AV40.4%
 
MAIN40.4%
 
AVALON30.3%
 
LASSEN20.2%
 
OLYMPIC BL20.2%
 
SAN PEDRO ST20.2%
 
HOLLYWOOD BL20.2%
 
SOTO20.2%
 
Other values (144)15215.2%
 
(Missing)81781.7%
 
2021-08-09T14:57:52.576088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique136 ?
Unique (%)74.3%
2021-08-09T14:57:52.740433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length34
Median length3
Mean length6.138
Min length3

Location
Categorical

HIGH CARDINALITY
UNIFORM

Distinct954
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
{'latitude': '33.9782', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.3294'}
 
3
{'latitude': '34.0215', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.2868'}
 
3
{'latitude': '34.2428', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.5361'}
 
3
{'latitude': '34.0554', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.2702'}
 
2
{'latitude': '34.0327', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.3002'}
 
2
Other values (949)
987 
ValueCountFrequency (%) 
{'latitude': '33.9782', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.3294'}30.3%
 
{'latitude': '34.0215', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.2868'}30.3%
 
{'latitude': '34.2428', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.5361'}30.3%
 
{'latitude': '34.0554', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.2702'}20.2%
 
{'latitude': '34.0327', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.3002'}20.2%
 
{'latitude': '34.2299', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.5536'}20.2%
 
{'latitude': '34.2336', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.4517'}20.2%
 
{'latitude': '33.9667', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.2893'}20.2%
 
{'latitude': '34.0618', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.2979'}20.2%
 
{'latitude': '34.0636', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.3047'}20.2%
 
Other values (944)97797.7%
 
2021-08-09T14:57:52.903249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique911 ?
Unique (%)91.1%
2021-08-09T14:57:53.044686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length139
Median length139
Mean length138.737
Min length129

Interactions

2021-08-09T14:57:24.671302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:24.813244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:24.952984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:25.077436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:25.216290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:25.355480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:25.496890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:25.635961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:25.791014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:25.980176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:26.139515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:26.277563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:26.421684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:26.526737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:26.631533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:26.753120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:26.870608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:26.998631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:27.112726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:27.234065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:27.357303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:27.487983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:27.599812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:27.713010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:27.823306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:27.924568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:28.034793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:28.148024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:28.270217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:28.388628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:28.505394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:28.646150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:29.378934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:29.492157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:29.632905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:29.756912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:29.869966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:29.996968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:30.125803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:30.258556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:30.391076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:30.526098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:30.678873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:30.828045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:30.964144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:31.120243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:31.256205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:31.406436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:31.544867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:31.679879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:31.829053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:31.974375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:32.117762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:32.268075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:32.439027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:32.616211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:32.754717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:32.885751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:33.021768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:33.162643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:33.314229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:33.475293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:33.621321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:33.762359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:33.921082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:34.073637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:34.227573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:34.550969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:34.670527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:34.789032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:34.922511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:35.109251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:35.284214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:35.442483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:35.597081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:35.738294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:35.877248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:36.005426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:36.144828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:36.262520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:36.379888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:36.510802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:36.648581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:36.786455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:36.909732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:37.022082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:37.150924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:37.273927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:37.385847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:37.521712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:37.650000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:37.773067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:37.912126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:38.045432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:38.195016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:38.355611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:38.512716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:38.687653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:38.857058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:39.001001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:39.143536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:39.285549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:39.415159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:39.557420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:39.692653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:39.845712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:39.990141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:40.149782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:40.443655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:40.613317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:40.942392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:41.101198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:41.212341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:41.319289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:41.445323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:41.574174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:41.830701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:41.960562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:42.096828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:42.260397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:42.402312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-08-09T14:57:53.160590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-09T14:57:53.434262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-09T14:57:53.701915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-09T14:57:53.986667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-08-09T14:57:54.618130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-08-09T14:57:42.708632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:44.310855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:44.644112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T14:57:44.919122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_indexDR NumberDate ReportedDate OccurredTime OccurredArea IDArea NameReporting DistrictCrime CodeCrime Code DescriptionMO CodesVictim AgeVictim SexVictim DescentPremise CodePremise DescriptionWeapon Used CodeWeapon DescriptionStatus CodeStatus DescriptionCrime Code 1Crime Code 2Crime Code 3Crime Code 4AddressCross StreetLocation
09829341421061222014-02-17T00:00:002014-02-17T00:00:00174521Topanga2147930CRIMINAL THREATS - NO WEAPON DISPLAYED042144FH501.0SINGLE FAMILY DWELLING511.0VERBAL THREATICInvest Cont930.0NaNNaNNaN20900 HART STNaN{'latitude': '34.1974', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.5884'}
114144451620135902016-07-14T00:00:002016-07-13T00:00:00190020Olympic2002442SHOPLIFTING - PETTY THEFT ($950 & UNDER)03440XX401.0MINI-MARTNaNNaNICInvest Cont442.0NaNNaNNaN400 N WESTERN AVNaN{'latitude': '34.0799', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.3113'}
218029311813188572018-09-08T00:00:002018-08-08T00:00:00170013Newton1363440THEFT PLAIN - PETTY ($950 & UNDER)2000 1814 0444 0416 0446 0417 032938FW505.0MOTEL400.0STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)ICInvest Cont440.0626.0NaNNaN5000 S CENTRAL AVNaN{'latitude': '33.9983', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.2608'}
33008591111171982011-09-04T00:00:002011-09-04T00:00:00121011Northeast1162433DRIVING WITHOUT OWNER CONSENT (DWOC)150129MB108.0PARKING LOTNaNNaNICInvest Cont433.0NaNNaNNaN1300 N VERMONT AVNaN{'latitude': '34.0956', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.2918'}
42592751106259882011-10-30T00:00:002011-10-30T00:00:00256Hollywood657626INTIMATE PARTNER - SIMPLE ASSAULT0400 200020FB502.0MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)400.0STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)AOAdult Other626.0NaNNaNNaN5600 SANTA MONICA BLNaN{'latitude': '34.0908', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.3113'}
53090741112141722011-05-24T00:00:002011-05-23T00:00:0022001277th Street1269510VEHICLE - STOLENNaN0NaNNaN101.0STREETNaNNaNICInvest Cont510.0NaNNaNNaN80TH STCENTRAL AV{'latitude': '33.9665', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.2564'}
614398331701096512017-03-25T00:00:002017-03-24T00:00:0019301Central185330BURGLARY FROM VEHICLE0344 160928MW101.0STREETNaNNaNICInvest Cont330.0NaNNaNNaN11TH STLOS ANGELES ST{'latitude': '34.0381', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.2566'}
72325701103205252011-09-13T00:00:002011-09-12T00:00:0015453Southwest358440THEFT PLAIN - PETTY ($950 & UNDER)0344 160720MH722.0COLLEGE/JUNIOR COLLEGE/UNIVERSITYNaNNaNICInvest Cont440.0NaNNaNNaN3600 TROUSDALE PYNaN{'latitude': '34.0215', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.2868'}
85773291219065642012-02-13T00:00:002012-02-12T00:00:00193019Mission1964956LETTERS, LEWD - TELEPHONE CALLS, LEWD150137FH502.0MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)NaNNaNICInvest Cont956.0NaNNaNNaN9400 VAN NUYS BLNaN{'latitude': '34.2412', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.4502'}
9520691016136202010-07-05T00:00:002010-01-01T00:00:00120016Foothill1672930CRIMINAL THREATS - NO WEAPON DISPLAYED0443 09136MH501.0SINGLE FAMILY DWELLINGNaNNaNICInvest Cont930.0NaNNaNNaN11900 PEORIA STNaN{'latitude': '34.2297', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.3924'}

Last rows

df_indexDR NumberDate ReportedDate OccurredTime OccurredArea IDArea NameReporting DistrictCrime CodeCrime Code DescriptionMO CodesVictim AgeVictim SexVictim DescentPremise CodePremise DescriptionWeapon Used CodeWeapon DescriptionStatus CodeStatus DescriptionCrime Code 1Crime Code 2Crime Code 3Crime Code 4AddressCross StreetLocation
99010243091503174842015-08-01T00:00:002015-07-31T00:00:0011303Southwest314310BURGLARY1601 1602 034424MW502.0MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)NaNNaNICInvest Cont310.0NaNNaNNaN3800 W 27TH STNaN{'latitude': '34.0312', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.3262'}
99119185111905116236212019-01-01T00:00:002019-06-21T00:00:0015505Harbor517901VIOLATION OF RESTRAINING ORDER1300 1309 203836FH501.0SINGLE FAMILY DWELLINGNaNNaNICInvest Cont901.0NaNNaNNaN1100 BROAD AVNaN{'latitude': '33.7849', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.2612'}
99217199101805189862018-11-25T00:00:002018-11-23T00:00:0018005Harbor507330BURGLARY FROM VEHICLE0344 1300 0329 160940FA101.0STREETNaNNaNICInvest Cont330.0NaNNaNNaN1300 HARMONY WYNaN{'latitude': '33.8209', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.2995'}
9934963351210205012012-12-21T00:00:002012-12-20T00:00:00100010West Valley1047901VIOLATION OF RESTRAINING ORDERNaN35FO501.0SINGLE FAMILY DWELLINGNaNNaNAOAdult Other901.0NaNNaNNaN5800 TEXHOMA AVNaN{'latitude': '34.1766', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.5159'}
99414877151705160282017-09-14T00:00:002017-09-14T00:00:0020405Harbor566901VIOLATION OF RESTRAINING ORDER181336FH502.0MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)NaNNaNAAAdult Arrest901.0NaNNaNNaN300 W 11TH STNaN{'latitude': '33.7342', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.2836'}
9958445811405161892014-10-16T00:00:002014-10-14T00:00:0020005Harbor567310BURGLARY0344 1602 035840FH707.0GARAGE/CARPORTNaNNaNICInvest Cont310.0NaNNaNNaN1700 S PACIFIC AVNaN{'latitude': '33.7288', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.288'}
9963242091113213882011-09-20T00:00:002011-09-20T00:00:00193513Newton1375440THEFT PLAIN - PETTY ($950 & UNDER)0100 0202 034419FH102.0SIDEWALKNaNNaNICInvest Cont440.0NaNNaNNaN58TH STCENTRAL AV{'latitude': '33.9902', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.2564'}
9971751491011005872010-01-25T00:00:002010-01-25T00:00:004011Northeast1125626INTIMATE PARTNER - SIMPLE ASSAULT0416 200034FW501.0SINGLE FAMILY DWELLING400.0STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)AAAdult Arrest626.0NaNNaNNaN4700 TOLAND WYNaN{'latitude': '34.12', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.2139'}
9985042701211200352012-10-27T00:00:002012-10-26T00:00:00180011Northeast1162442SHOPLIFTING - PETTY THEFT ($950 & UNDER)032524MB402.0MARKETNaNNaNAOAdult Other442.0NaNNaNNaN4500 W SUNSET BLNaN{'latitude': '34.0982', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.2874'}
99918917371913044332019-01-07T00:00:002018-11-15T00:00:00120013Newton1371649DOCUMENT FORGERY / STOLEN FELONY092336MH501.0SINGLE FAMILY DWELLINGNaNNaNICInvest Cont649.0NaNNaNNaN100 W 56TH STNaN{'latitude': '33.9915', 'needs_recoding': False, 'human_address': '{"address":"","city":"","state":"","zip":""}', 'longitude': '-118.2739'}